{"cells":[{"cell_type":"code","execution_count":15,"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","collapsed":true,"trusted":true},"outputs":[],"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n","# For example, here's several helpful packages to load in \n","\n","#import the libraries\n","import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","import matplotlib.pyplot as plt #Data Visualization \n","import seaborn as sns  #Python library for Vidualization\n","\n","\n","# Input data files are available in the \"../input/\" directory.\n","# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n","\n","import os\n","#print(os.listdir(\"../input\"))\n","\n","# Any results you write to the current directory are saved as output."]},{"cell_type":"code","execution_count":17,"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_kg_hide-input":false,"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","collapsed":true,"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sepal_length</th>\n","      <th>sepal_width</th>\n","      <th>petal_length</th>\n","      <th>petal_width</th>\n","      <th>species</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>5.1</td>\n","      <td>3.5</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4.9</td>\n","      <td>3.0</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4.7</td>\n","      <td>3.2</td>\n","      <td>1.3</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.6</td>\n","      <td>3.1</td>\n","      <td>1.5</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>3.6</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5.4</td>\n","      <td>3.9</td>\n","      <td>1.7</td>\n","      <td>0.4</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>4.6</td>\n","      <td>3.4</td>\n","      <td>1.4</td>\n","      <td>0.3</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>5.0</td>\n","      <td>3.4</td>\n","      <td>1.5</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>4.4</td>\n","      <td>2.9</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>4.9</td>\n","      <td>3.1</td>\n","      <td>1.5</td>\n","      <td>0.1</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   sepal_length  sepal_width  petal_length  petal_width      species\n","0           5.1          3.5           1.4          0.2  Iris-setosa\n","1           4.9          3.0           1.4          0.2  Iris-setosa\n","2           4.7          3.2           1.3          0.2  Iris-setosa\n","3           4.6          3.1           1.5          0.2  Iris-setosa\n","4           5.0          3.6           1.4          0.2  Iris-setosa\n","5           5.4          3.9           1.7          0.4  Iris-setosa\n","6           4.6          3.4           1.4          0.3  Iris-setosa\n","7           5.0          3.4           1.5          0.2  Iris-setosa\n","8           4.4          2.9           1.4          0.2  Iris-setosa\n","9           4.9          3.1           1.5          0.1  Iris-setosa"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["#Import the dataset\n","\n","dataset = pd.read_csv('./IRIS.csv')\n","\n","#Exploratory Data Analysis\n","#As this is unsupervised learning so Label (Output Column) is unknown\n","\n","dataset.head(10) #Printing first 10 rows of the dataset\n"]},{"cell_type":"code","execution_count":18,"metadata":{"_uuid":"d47849badb0035c1968d9b19384a266f6cfdc634","collapsed":true,"trusted":true},"outputs":[{"data":{"text/plain":["(150, 5)"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["#total rows and colums in the dataset\n","dataset.shape"]},{"cell_type":"code","execution_count":19,"metadata":{"_uuid":"bcfc2fb58c8989f2f548dbf19d83cbf7bcc42313","collapsed":true,"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 150 entries, 0 to 149\n","Data columns (total 5 columns):\n"," #   Column        Non-Null Count  Dtype  \n","---  ------        --------------  -----  \n"," 0   sepal_length  150 non-null    float64\n"," 1   sepal_width   150 non-null    float64\n"," 2   petal_length  150 non-null    float64\n"," 3   petal_width   150 non-null    float64\n"," 4   species       150 non-null    object \n","dtypes: float64(4), object(1)\n","memory usage: 6.0+ KB\n"]}],"source":["dataset.info() # there are no missing values as all the columns has 200 entries properly"]},{"cell_type":"code","execution_count":20,"metadata":{"_uuid":"ca96e1a193d3818013ccffa63b90914c1042cac6","collapsed":true,"trusted":true},"outputs":[{"data":{"text/plain":["sepal_length    0\n","sepal_width     0\n","petal_length    0\n","petal_width     0\n","species         0\n","dtype: int64"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["#Missing values computation\n","dataset.isnull().sum()"]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sepal_length</th>\n","      <th>sepal_width</th>\n","      <th>petal_length</th>\n","      <th>petal_width</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>5.1</td>\n","      <td>3.5</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4.9</td>\n","      <td>3.0</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4.7</td>\n","      <td>3.2</td>\n","      <td>1.3</td>\n","      <td>0.2</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.6</td>\n","      <td>3.1</td>\n","      <td>1.5</td>\n","      <td>0.2</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>3.6</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5.4</td>\n","      <td>3.9</td>\n","      <td>1.7</td>\n","      <td>0.4</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>4.6</td>\n","      <td>3.4</td>\n","      <td>1.4</td>\n","      <td>0.3</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>5.0</td>\n","      <td>3.4</td>\n","      <td>1.5</td>\n","      <td>0.2</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>4.4</td>\n","      <td>2.9</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>4.9</td>\n","      <td>3.1</td>\n","      <td>1.5</td>\n","      <td>0.1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   sepal_length  sepal_width  petal_length  petal_width\n","0           5.1          3.5           1.4          0.2\n","1           4.9          3.0           1.4          0.2\n","2           4.7          3.2           1.3          0.2\n","3           4.6          3.1           1.5          0.2\n","4           5.0          3.6           1.4          0.2\n","5           5.4          3.9           1.7          0.4\n","6           4.6          3.4           1.4          0.3\n","7           5.0          3.4           1.5          0.2\n","8           4.4          2.9           1.4          0.2\n","9           4.9          3.1           1.5          0.1"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["data=dataset.drop(['species'],axis=1)\n","data.head(10)"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 150 entries, 0 to 149\n","Data columns (total 4 columns):\n"," #   Column        Non-Null Count  Dtype  \n","---  ------        --------------  -----  \n"," 0   sepal_length  150 non-null    float64\n"," 1   sepal_width   150 non-null    float64\n"," 2   petal_length  150 non-null    float64\n"," 3   petal_width   150 non-null    float64\n","dtypes: float64(4)\n","memory usage: 4.8 KB\n"]}],"source":["data.info()"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/plain":["(150, 4)"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["data.shape"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sepal_length</th>\n","      <th>sepal_width</th>\n","      <th>petal_length</th>\n","      <th>petal_width</th>\n","      <th>species</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>5.1</td>\n","      <td>3.5</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4.9</td>\n","      <td>3.0</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4.7</td>\n","      <td>3.2</td>\n","      <td>1.3</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.6</td>\n","      <td>3.1</td>\n","      <td>1.5</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>3.6</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5.4</td>\n","      <td>3.9</td>\n","      <td>1.7</td>\n","      <td>0.4</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>4.6</td>\n","      <td>3.4</td>\n","      <td>1.4</td>\n","      <td>0.3</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>5.0</td>\n","      <td>3.4</td>\n","      <td>1.5</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>4.4</td>\n","      <td>2.9</td>\n","      <td>1.4</td>\n","      <td>0.2</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>4.9</td>\n","      <td>3.1</td>\n","      <td>1.5</td>\n","      <td>0.1</td>\n","      <td>Iris-setosa</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   sepal_length  sepal_width  petal_length  petal_width      species\n","0           5.1          3.5           1.4          0.2  Iris-setosa\n","1           4.9          3.0           1.4          0.2  Iris-setosa\n","2           4.7          3.2           1.3          0.2  Iris-setosa\n","3           4.6          3.1           1.5          0.2  Iris-setosa\n","4           5.0          3.6           1.4          0.2  Iris-setosa\n","5           5.4          3.9           1.7          0.4  Iris-setosa\n","6           4.6          3.4           1.4          0.3  Iris-setosa\n","7           5.0          3.4           1.5          0.2  Iris-setosa\n","8           4.4          2.9           1.4          0.2  Iris-setosa\n","9           4.9          3.1           1.5          0.1  Iris-setosa"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["dataset.head(10)"]},{"cell_type":"code","execution_count":10,"metadata":{"_uuid":"5e4f9f5b66017fb178608632d9f9bc07cc825853","collapsed":true,"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["e:\\Programs\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n","  warnings.warn(\n","e:\\Programs\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n","  warnings.warn(\n","e:\\Programs\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n","  warnings.warn(\n","e:\\Programs\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n","  warnings.warn(\n","e:\\Programs\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n","  warnings.warn(\n"]}],"source":["#Building the Model\n","#KMeans Algorithm to decide the optimum cluster number , KMeans++ using Elbow Mmethod\n","#to figure out K for KMeans, I will use ELBOW Method on KMEANS++ Calculation\n","from sklearn.cluster import KMeans\n","from sklearn.preprocessing import StandardScaler\n","\n","wcss=[]\n","#we always assume the max number of cluster would be 10\n","#you can judge the number of clusters by doing averaging\n","###Static code to get max no of clusters\n","data=dataset.drop(['species'],axis=1)\n","\n","# 标准化数据\n","scaler = StandardScaler()\n","data_scaled = scaler.fit_transform(data)\n","\n","for i in range(1,6):\n","    kmeans = KMeans(n_clusters= i, init='k-means++', n_init=10, random_state=0)\n","    kmeans.fit(data_scaled)\n","    wcss.append(kmeans.inertia_)\n","    #inertia_ is the formula used to segregate the data points into clusters"]},{"cell_type":"code","execution_count":24,"metadata":{"_uuid":"3a8226bd446bfce88342d98fea5d00d5c7b57a9e","collapsed":true,"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["#Visualizing the ELBOW method to get the optimal value of K \n","plt.plot(range(1,6), wcss)\n","plt.title('The Elbow Method')\n","plt.xlabel('no of clusters')\n","plt.ylabel('wcss')\n","plt.show()"]},{"cell_type":"code","execution_count":25,"metadata":{"_uuid":"5b0ff8bf00fb9e9635e1c5302ffdbc2e9abd517e","collapsed":true,"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["e:\\Programs\\Anaconda\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n","  warnings.warn(\n"]}],"source":["#If you zoom out this curve then you will see that last elbow comes at k=5\n","#no matter what range we select ex- (1,21) also i will see the same behaviour but if we chose higher range it is little difficult to visualize the ELBOW\n","#that is why we usually prefer range (1,11)\n","##Finally we got that k=5\n","\n","#Model Build\n","kmeansmodel = KMeans(n_clusters= 3, init='k-means++', random_state=0)\n","y_kmeans= kmeansmodel.fit_predict(data)\n","\n","#For unsupervised learning we use \"fit_predict()\" wherein for supervised learning we use \"fit_tranform()\"\n","#y_kmeans is the final model . Now how and where we will deploy this model in production is depends on what tool we are using.\n","#This use case is very common and it is used in BFS industry(credit card) and retail for customer segmenattion."]},{"cell_type":"code","execution_count":26,"metadata":{},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'PCA Cluster Plot')"]},"execution_count":26,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 700x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["from sklearn.decomposition import PCA\n","pca = PCA(n_components=2).fit_transform(data)  \n","\n","f, ax = plt.subplots(1, figsize=(7, 6))\n","\n","ax.scatter(pca[:, 0], pca[:, 1], c=kmeansmodel.labels_)\n","ax.set_title('PCA Cluster Plot')"]},{"cell_type":"code","execution_count":27,"metadata":{},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'TSNE Cluster Plot')"]},"execution_count":27,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 700x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["from sklearn.manifold import TSNE\n","tsne = TSNE().fit_transform(data)\n","f, ax = plt.subplots(1, figsize=(7, 6))\n","\n","ax.scatter(tsne[:, 0], tsne[:, 1], c=kmeansmodel.labels_)\n","ax.set_title('TSNE Cluster Plot')"]}],"metadata":{"kernelspec":{"display_name":"Python 3.9.12 ('base')","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.7"},"vscode":{"interpreter":{"hash":"ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"}}},"nbformat":4,"nbformat_minor":1}
